Keywords:2009 H1N1 Non-pharmaceutical intervention Model of goal-directed behavior Travel intention a b s t r a c t Theoretically, in the tourism context this study introduced a new concept of non-pharmaceutical intervention (NPI) for influenza, and tested the impact of NPI on the behavioral intention of potential international tourists. This study also extended the model of goal-directed behavior (MGB) by incorporating the new concepts of NPI, and the perception of 2009 H1N1. The model found that desire, perceived behavioral control, frequency of past behavior, and non-pharmaceutical interventions predicted tourists' intention but perceptions of 2009 H1N1 had nil effect on desire and intention. Personal non-pharmaceutical interventions were theorized as adaptive behavior of tourists intending to travel during a pandemic which should be supported by tourism operators on a system-wide basis. Practically, this study dealt with the issue of influenza 2009 H1N1 with the study findings and implications providing government agencies, tourism marketers, policy-makers, transport systems, and hospitality services with important suggestions for NPI and international tourism during pandemics.

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Tourist arrivals and tourist expenditure, in both aggregate and per capita forms, are commonly used measures of tourism demand in empirical research. This study compares these two measures in the context of econometric modelling and the forecasting of tourism demand. The empirical study focuses on demand for Hong Kong tourism by residents of Australia, the UK and the USA. Using the general-to-specific modelling approach, key determinants of tourism demand are identified based on different demand measures. In addition, the forecasting accuracy of these demand measures is examined. It is found that tourist arrivals in Hong Kong are influenced mainly by tourists' income and 'word-of-mouth'/habit persistence effects, while the tourism price in Hong Kong relative to that of the tourist origin country is the most important determinant of tourist expenditure in Hong Kong. Moreover, the aggregate tourism demand models outperform the per capita models, with aggregate expenditure models being the most accurate. The implications of these findings for tourism decision making are that the choice of demand measure for forecasting models should depend on whether the objective of the decision maker is to maximize tourist arrivals or expenditure (receipts), and also that the models should be specified in aggregate form.

The profound impact of the coronavirus disease 2019 (COVID-19) pandemic on global tourism activity has rendered forecasts of tourism demand obsolete. Accordingly, scholars have begun to seek the best methods to predict the recovery of tourism from the devastating effects of COVID-19. In this study, econometric and judgmental methods were combined to forecast the possible paths to tourism recovery in Hong Kong. The autoregressive distributed lag-error correction model was used to generate baseline forecasts, and Delphi adjustments based on different recovery scenarios were performed to reflect different levels of severity in terms of the pandemic's influence. These forecasts were also used to evaluate the economic effects of the COVID-19 pandemic on the tourism industry in Hong Kong.

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A general to specific methodology is used to construct UK demand for outbound tourism models to twelve destinations. A tourism destination preference index is introduced to take into account social, cultural and psychological influences on tourists' decisions concerning their overseas holiday destinations. The tests support the existence of a cointegration relationship for each of 11 UK overseas holiday destinations. The corresponding error correction models are estimated. The empirical results show that the long-run income elasticities for all destinations range from 1.70 to 3.90 with an average of 2.367. The lowest and highest short-run income elasticities are 1.05 and 3.78 respectively, with an average of 2.216. The estimates of the income elasticities imply that overseas holidays are highly income elastic while the own-price elasticities suggest that the demand for UK outbound tourism is relatively own-price inelastic. In terms of the significance of substitution prices in the regression equations, Ireland is the favourite substitute destination for UK outbound tourists. Ex post forecasts over a period of six years are generated from the ECM models and the results compared with those of a naive model, an AR(1) model, an ARMA(p,q) model, and a VAR model. The forecasting performance criteria show that the ECM model has the best overall forecasting performance for UK outbound tourism.